An Introduction to Audio and Visual Research and Applications in Marketing

Li, Xiao; Kim, Hye-jin; Ding, Min

doi:10.1108/s1548-6435(2013)0000010012

Cited by 15 publications

(12 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The CaF belongs to the research domain called “artificial empathy,” defined as “the ability of nonhuman models to predict a person’s internal states (e.g., cognitive, affective, physical) given the signals he or she emits (e.g., facial expression, voice, gesture) or to predict a person’s reaction (including, but not limited to internal states) when he or she is exposed to a given set of stimuli (e.g., facial expression, voice, gesture, graphics, music, etc. )” (Xiao, Kim, and Ding 2013, p. 244) . The CaF framework enables us to predict how a person will react when they see someone’s face, without revealing the face itself.…”

Section: Discussionmentioning

confidence: 99%

“…For example, face-based perceptions such as maturity, dominance, and competence have been shown to influence the selection of employees (e.g., Gorn, Jiang, and Johar 2008; Graham, Harvey, and Puri 2016; Keh et al 2013). Second, perceptions from customer facial data can be used to improve targeting and segmentation, firms’ understanding of customer preferences, and marketing effectiveness (Xiao, Kim, and Ding 2013). For example, Lu, Xiao, and Ding (2016) analyzed customers’ facial expressions in videos to infer the customers’ product preferences.…”

Section: Literature Review and Motivationmentioning

confidence: 99%

See 1 more Smart Citation

Contour-as-Face Framework: A Method to Preserve Privacy and Perception

Zhou¹,

Lu²,

Ding³

2020

Journal of Marketing Research

Self Cite

View full text Add to dashboard Cite

Consumers and marketers use facial information to make important inferences about others in many business contexts. However, consumers and firms are increasingly concerned about privacy and discrimination. To address privacy–perception trade-offs, the authors propose a novel contour-as-face (CaF) framework that transforms face images into contour images incorporating both the nonoutline and outline features of facial parts. In three empirical studies, the authors (1) compare human perceptions of face and contour images along 15 dimensions commonly assessed in marketing contexts; (2) investigate the effectiveness of contour images for protecting anonymity related to identity, age, and gender; and (3) implement the CaF framework in a real-life online dating context. Results show that the CaF framework effectively resolves privacy–perception trade-off problems by preserving the information that is useful for humans to make inferences about many relevant perceptual dimensions in marketing while making it virtually impossible for humans to infer identity and very difficult to infer age and gender accurately—two critical discrimination factors. Results from the field implementation demonstrate the feasibility and value of using the CaF framework for real-life decision making.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Literature Review and Motivationmentioning

confidence: 99%

Contour-as-Face Framework: A Method to Preserve Privacy and Perception

Zhou¹,

Lu²,

Ding³

2020

Journal of Marketing Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…FaceReader has been used to capture emotions in previous research by, for example, Danner, Sidorkina, Joechl, and Duerrschmid (2013) and de Wijk, Kooijman, Verhoeven, Holthuysen, and de Graaf (2012). A main assumption is that the individual's facial expressions provide useful indications of the individual's emotions (Xiao, Kim, and Ding 2013). The level of the seven emotion variables (range 0-1; the higher the value, the higher the emotional intensity) are computed 30 times per second.…”

Section: Methodsmentioning

confidence: 99%

Jokes in the store and its effects on customer satisfaction

Söderlund

Oikarinen

Heikka

2017

The International Review of Retail, Distribution and Consumer R

View full text Add to dashboard Cite

This study assesses the impact of employee use of jokes embedded in face-to-face conversations with customers. Our main finding-derived from experiments in which employee use of jokes was manipulated-was that jokes reduced customer satisfaction. This reduction occurred regardless if jokes was related or unrelated to the employee's overall message. Our results also indicate that the use of jokes had a negative impact on perceived message relevance, and that perceived relevance mediated the link between employee use of jokes and customer satisfaction. In addition, our results show that no joke usage versus joke usage did not produce different emotional responses. This suggests that the impact of jokes on affect is attenuated when jokes are embedded in conversational content.

show abstract

“…Thanks to advanced computer vision techniques, a customer's facial expressions and the areas of a garment that a customer touches can both be automatically inferred from video data with reasonable accuracy (see a recent review by Xiao et al 2013). We describe the analysis process below (see Figure 2).…”

Section: Step 1: Infer Preferences Frommentioning

confidence: 99%

“…Applications of video analysis in marketing may include detecting gender, skin color, and eye gaze; recognizing facial expressions and body or hand gestures; tracking trajectories; and counting people (see Xiao et al 2013). As a first attempt to use video analysis to infer customers' individual preferences, the VAR system may yield benefits such as reducing customer searching effort, increasing retail sales by recommending garments that customers are likely to purchase, and helping companies adjust designs or inventory to match customer preferences.…”

Section: Figurementioning

confidence: 99%

A Video-Based Automated Recommender (VAR) System for Garments

Ding

2016

Marketing Science

Self Cite

View full text Add to dashboard Cite

I n this paper, we propose an automated and scalable garment recommender system using real-time in-store videos that can improve the experiences of garment shoppers and increase product sales. The video-based automated recommender (VAR) system is based on observations that garment shoppers tend to try on garments and evaluate themselves in front of store mirrors. Combining state-of-the-art computer vision techniques with marketing models of consumer preferences, the system automatically identifies shoppers' preferences based on their reactions and uses that information to make meaningful personalized recommendations. First, the system uses a camera to capture a shopper's behavior in front of the mirror to make inferences about her preferences based on her facial expressions and the part of the garment she is examining at each time point. Second, the system identifies shoppers with preferences similar to the focal customer from a database of shoppers whose preferences, purchasing, and/or consideration decisions are known. Finally, recommendations are made to the focal customer based on the preferences, purchasing, and/or consideration decisions of these like-minded shoppers. Each of the three steps can be implemented with several variations, and a retailing chain can choose the specific configuration that best serves its purpose. In this paper, we present an empirical test that compares one specific type of VAR system implementation against two alternative, nonautomated personal recommender systems: self-explicated conjoint (SEC) and self-evaluation after try-on (SET). The results show that VAR consistently outperforms SEC and SET. A second empirical study demonstrates the feasibility of VAR in real-time applications. Participants in the second study enjoyed the VAR experience, and almost all of them tried on the recommended garments. VAR should prove to be a valuable tool for both garment retailers and shoppers.Data, as supplemental material, are available at http://dx.

show abstract

An Introduction to Audio and Visual Research and Applications in Marketing

Cited by 15 publications

References 83 publications

Contour-as-Face Framework: A Method to Preserve Privacy and Perception

Contour-as-Face Framework: A Method to Preserve Privacy and Perception

Jokes in the store and its effects on customer satisfaction

A Video-Based Automated Recommender (VAR) System for Garments

Contact Info

Product

Resources

About